4 traits good data scientists share

Big data is creating a booming market for data scientists as companies struggle to not only store loads of data, but also analyze it and interpret it. To be a good data scientist, you'll want to master these four areas.

Big data has been a common phrase in the tech industry as companies of all types collect staggering amounts of customer. Data is also a high-profile topic as recent security breaches that leaked sensitive personally identifiable information to the public. Aside from the security issues of protecting all that data, companies are quickly finding that, while they can collect massive amounts of information, it's another thing to actually organize and analyze it.

Data isn't relevant only to IT departments, either. It touches on a number of disciplines, not just those that require a computer science background. "There's a trend where the data scientist is the new DBA, so 20 years, 25 years ago, DBAs were the new type of career, so to speak, and we're seeing the same thing happen where there's a new type of category called data science. What you'll find is that this new type of data science field is an amalgam of different types of math, different types of science, and different types of software and computer science," says Erick Frenkiel, CEO at MemSQL.

And data grows, so does the need for professionals who can help make sense of the massive amounts of information storming business' servers. Data scientists are in demand, and if you're considering breaking into the field, it's a great time. Here are four important traits that Frenkiel has identified after interviewing a number of professionals working in big data.

Candidates are open-minded

When it comes to technology, there is always more than one way to get the results you want, and if you keep that in mind, it will help you become a data scientist. As Frenkiel points out, there are a number of technology platforms, and more emerging each day, so getting stuck in your ways isn't your best bet to become successful in this industry.

Frenkiel dubs this the "multi-model approach," and says "that even as technology evolves you still have multiple ways of interacting with software and its incredibly important to have more than one, or a few tools in your bag, you really do want a lot of familiarity with an entire ecosystem and then a specialty for yourself in a given part of that eco system."

Data scientists need to stay nimble and open-minded in order to find the best ways to tackle massive amounts of data. And it will also come in handy to find new and helpful ways to actually use that data to drive the further success in the business.

Know why you're in the industry

You don't want to get a job in an industry without knowing why you're doing it; if it's just because of perceived job security and a high salary, you aren't in it for the right reasons. You want to fully understand the breadth of data and its impact on society, not just the business you work for. While companies might be data hungry, there are also questions about security and privacy that arise when dealing with customers' personal information. Keep your eye on the positive impacts data can have and try to keep your focus on how it can help better your business, as well as the world.

Frenkiel also notes the importance of passion, and not just in data jobs, but in every career, "At the end of the day what matters is passion. Life is too short to work somewhere where you don't have the passion for what you do."

Networking, networking, networking

This might go for any job, but networking can go a long way in helping shape your career in big data. You'll want to engage with the industry by leaving your desk and heading to conferences and other meetings that focus on data. Meeting professionals in the industry and staying on top of the latest technology through social media, conferences and personal meetings can not only help you in your current job, but can help you with future prospects as well.

Stay current

You'll need to stay on top of your game when it comes to big data. Try out new and interesting software and other tools, and stay current on sites like GitHub, where you can get free copies of software. This way you'll know what works and what doesn't work, for your company, and you won't find yourself left behind when a new toolkit is released.

You don't need to consider going back to school to get into big data either, because it isn't really dedicated to just IT or technology. Data touches nearly every department in a company, so your background in business or marketing can help you just as much as a background in computer science.

Frenkiel suggests "hacking your way into proficiency," which means heading to Amazon to find a book that will teach you SQL. From there, as long as you can back up your experience and knowledge, you can consider a career in big data. "If you're in marketing operations or sales operations or a project manager you can definitely pick up these big data skills now, without having to go into a classroom," Frenkiel says.

This story, "4 traits good data scientists share" was originally published by
CIO.

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